Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Authors: Yash Jain, Harkirat Behl, Zsolt Kira, Vibhav Vineet
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Universal Object-Detection Benchmark show that we outperform the existing state-of-the-art by average +10.2 AP score and improve over our non-Mo E baseline by average +2.0 AP score. |
| Researcher Affiliation | Collaboration | Yash Jain1 Harkirat Behl2 Zsolt Kira1 Vibhav Vineet2 1Georgia Institute of Technology 2Microsoft Research |
| Pseudocode | No | The paper describes methods using mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/jinga-lala/DAMEX. |
| Open Datasets | Yes | UODB comprises of 11 datasets: Pascal VOC [5], Wider Face [40], KITTI [8], LISA [26], DOTA [36], COCO [22], Watercolor, Clipart, Comic [13], Kitchen [9] and Deep Lesions [38], shown in Figure 1. |
| Dataset Splits | Yes | All the reported numbers in this work are mean Average Precision (AP) scores evaluated on the available test or val set of corresponding dataset. |
| Hardware Specification | Yes | We kept one expert per GPU and train on 8 RTX6000 GPUs with a batch-size of 2 per GPU, unless mentioned otherwise. |
| Software Dependencies | No | The paper mentions using the 'TUTEL library [12]' but does not provide specific version numbers for this or any other software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For hyper-parameters, as in DINO, we use a 6-layer Transformer encoder and a 6-layer Transformer decoder and 256 as the hidden feature dimension. We use a capacity factor f of 1.25 and an auxiliary expert-balancing loss weight of 0.1 with top-1 selection of experts. We use a learning-rate of 1.4e-4 and kept other DINO-specific hyperparameters same as [42]. |